We represent natural language semantics by combining
logical and distributional information in probabilistic logic.
We use Markov Logic Networks (MLN) for the RTE task, and
Probabilistic Soft Logic (PSL) for the STS task. The system
is evaluated on the SICK dataset. Our best system achieves
73% accuracy on the RTE task, and a Pearson's correlation
of 0.71 on the STS task.